Automatic Summarization of textual content can be used to save time for end users by providing an overview of textual content (e.g., a document or an article) which can be quickly read by the user. Conventional extractive summarization techniques extract out key phrases from the input textual content, and then select a subset of these phrases to place in the summary. Summaries generated by these conventional summarization techniques, however, are often not human like. Furthermore, some conventional summarization techniques generate a summary, and then can “tune” the summary to a target audience as a post processing step after generation of the summary. However, tuning a summary to a target audience after the summary is generated often results in changing the meaning of the original text. Consider, for example, the sentence “the entire journey is bigger than the team”. Based on a linguistic preference of a target audience, the word “total” may be preferred over word “entire”, and the word “travel” may be preferred over the word “journey”. While both of these words are fine replacements for the original word, a resulting sentence formed by replacing these words, e.g., “the total travel is bigger than the team”, does not have the same meaning as the original sentence. Furthermore, existing summarization techniques are unable to generate multiple summaries which are tuned to different target audience vocabularies.